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Publikováno v:
ICASSP
Symmetric block partitioned tensors (SBPT) are a useful structure in signal processing applications, often generated from computing higher-order statistics on observed data. Such tensors often follow the rank (Rm, Rm, 1) SBPT structure, but in some a
Publikováno v:
ICASSP
The objective of this work is to consider sparse representations of certain classes of signals on circulant graphs, by introducing families of graph wavelets which possess vanishing (exponential) moment properties. In light of this, we propose a nove
Publikováno v:
ICASSP
T-SNE is a well-known approach to embedding high-dimensional data and has been widely used in data visualization. The basic assumption of t-SNE is that the data are non-constrained in the Euclidean space and the local proximity can be modelled by Gau
Publikováno v:
ICASSP
We address the problem of estimating a low-rank positive semidefinite (PSD) matrix from a set of magnitude measurements that are quadratic in the sensing vectors in the presence of arbitrary outliers. We propose a parameter-free algorithm that seeks
Publikováno v:
ICASSP
We study stochastic linear optimization problem with bandit feedback. The set of arms take values in an N-dimensional space and belongs to a bounded polyhedron described by finitely many linear inequalities. We present an algorithm that has O(Nlog1+e
Autor:
Christian Jutten, Dana Lakat
Publikováno v:
ICASSP
In this paper, we present an alternative proof for characterizing the (non-) identifiability conditions of independent vector analysis (IVA). IVA extends blind source separation to several mixtures by taking into account statistical dependencies betw
Publikováno v:
ICASSP
We study the problem of high-dimensional covariance matrix estimation from partial observations. We consider covariance matrices modeled as Kronecker products of matrix factors, and rely on observations with missing values. In the absence of missing